Publication Chronicle

Students with bold-font names are primarily advised by me when doing the corresponding project.

Year 2025

• Haoteng Yin, Rongzhe Wei, Eli Chien, Pan Li, Privately Learning from Graphs with Applications in Fine-tuning Large Language Models (code), COLM 2025

• Haoyu Wang, Shikun Liu, Rongzhe Wei, Pan Li, Generalization Principles for Inference over Text-Attributed Graphs with Large Language Models (code) ICML 2025

• Jiajun Zhu, Peihao Wang, Ruisi Cai, Jason D. Lee, Pan Li, Zhangyang Wang, Rethinking Addressing in Language Models via Contextualized Equivariant Positional Encoding (code) ICML 2025

• Rongzhe Wei, Mufei Li, Mohsen Ghassemi, Eleonora Kreacic, Yifan Li, Xiang Yue, Bo Li, Vamsi K. Potluru, Pan Li, Eli Chien, Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning (code) ICML 2025

• Yinan Huang, Haoyu Wang, Pan Li, What Are Good Positional Encodings for Directed Graphs? ICLR 2025 (codes)

• Eli Chien, Pan Li, Convergent privacy loss of noisy-sgd without convexity and smoothness ICLR 2025 

• Mufei Li*, Siqi Miao*, Pan Li, Simple is effective: The roles of graphs and large language models in knowledge-graph-based retrieval-augmented generation ICLR 2025  (codes) + Oral at the FM-Wild Workshop

• Peihao Wang, Ruisi Cai, Yuehao Wang, Jiajun Zhu, Pragya Srivastava, Zhangyang Wang, Pan Li, Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing ICLR 2025 (codes)

• Mufei Li, Viraj Shitole, Eli Chien, Changhai Man, Zhaodong Wang, Srinivas Sridharan, Ying Zhang, Tushar Krishna, Pan Li, LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation ICLR 2025 (codes) (Spotlight at ICLR 2025 + Oral at the SynthData Workshop)

• Jiajun Zhu*, Siqi Miao*, Rex Ying, Pan Li, Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning Nature Machine Intelligence 

Year 2024

• Eli Chien, Haoyu Wang, Ziang Chen, Pan Li, Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning (Spotlight), NeurIPS, 2024

• Rongzhe Wei, Eli Chien, Pan Li, Differentially Private Graph Diffusion with Applications in Personalized PageRanks, NeurIPS, 2024

• Eli Chien, Haoyu Wang, Ziang Chen, Pan Li, Certified Machine Unlearning via Noisy Stochastic Gradient Descent, NeurIPS, 2024

• Deyu Zou*, Shikun Liu*, Siqi Miao, Victor Fung, Shiyu Chang, Pan Li, GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shift (Benchmark Track), NeurIPS, 2024 (codes)

• Mufei Li, Eleonora Kreacic, Vamsi K. Potluru, Pan Li, GraphMaker: Can Diffusion Models Generate Large Attributed Graphs, TMLR, 2024 (codes)

• Eli Chien, Mufei Li, Anthony Aportela, Kerr Ding, Shuyi Jia, Supriyo Maji, Zhongyuan Zhao, Javier Duarte, Victor Fung, Callie Hao, Yunan Luo, Olgica Milenkovic, David Pan, Santiago Segarra, Pan Li, "Exploring the Opportunities and Challenges of Graph Neural Networks in Electrical Engineering," in Nature Reviews Electrical Engineering, 2024

• Siqi Miao, Zhiyuan Lu, Mia Liu, Javier Duarte, Pan Li, Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics, ICML 2024 (codes, oral)

• Shikun Liu, Deyu Zou, Han Zhao, Pan Li, Pairwise Alignment Improves Graph Domain Adaptation, ICML 2024 (codes, spotlight)

• Xiyuan Wang, Pan Li, Muhan Zhang, Graph As Point Set, ICML 2024

• Amit Roy, Juan Shu, Jia Li, Carl Yang, Olivier Elshocht, Jeroen Smeets, Pan Li, GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction, WSDM 2024 (code)

• Tianyi Zhang*, Haoteng Yin*, Rongzhe Wei, Pan Li, Anshumali Shrivastava, "Learning Scalable Structural Representations for Link Prediction with Bloom Signatures," WWW 2024 (codes)

• Haoyu Wang, Jialin Liu, Xiaohan Chen, Xinshang Wang, Pan Li, Wotao Yin, DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee, TMLR 2024 (codes)

• Rongzhe Wei, Eleonora Kreacic, Haoyu Wang, Haoteng Yin, Eli Chien, Vamsi K. Potluru, Pan Li, On the Inherent Privacy Properties of Discrete Denoising Diffusion Models. TMLR, WSDAIF 2024 Oral 

• Yinan Huang*, William Lu*, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, Pan Li, "On the Stability of Expressive Positional Encodings for Graphs, " ICLR 2024 (codes)

• Peihao Wang, Shenghao Yang, Shu Li, Zhangyang Wang, Pan Li, "Polynomial Width is Sufficient for Set Representation with High-dimensional Features, " ICLR 2024

• Tianci Liu, Haoyu Wang, Feijie Wu, Hengtong Zhang, Pan Li, Lu Su, Jing Gao, "Towards Poisoning Fair Representations, " ICLR 2024 

• Changlin Wan, Muhan Zhang, Pengtao Dang, Wei Hao, Sha Cao, Pan Li and Chi Zhang, "Ambiguities in Neural-Network-based Hyperedge Prediction", Journal of Applied and Computational Topology, 2024

Year 2023

• Eli Chien, Wei-Ning Chen, Chao Pan, Pan Li, Ayfer Özgür, Olgica Milenkovic, "Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection, " NeurIPS 2023 code 

• Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiang Qiu, Pan Li, "Structural Re-weighting Improves Graph Domain Adaptation, " ICML 2023.  (codes) 

• Siqi Miao, Yunan Luo, Mia Liu, Pan Li, "Interpretable Geometric Deep Learning via Learnable Randomness Injection,"  ICLR 2023. (codes)

• Haoyu Wang, Pan Li, "Unsupervised Learning for Combinatorial Optimization Needs Meta-Learning,"  ICLR 2023. (codes)

• Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, Pan Li, "Equivariant Hypergraph Diffusion Neural Operators,"  ICLR 2023. (codes)

• Tianchun Li*, Shikun Liu*, Yongbin Feng*, Garyfallia Paspalaki , Nhan V. Tran, Miaoyuan Liu , Pan Li, "Semi-supervised Graph Neural Network for Particle-level Noise Removal," The European Physics Journal C 2023, a short version appeared at NeurIPS AI4Science workshop 2021. 

• Haoteng Yin, Muhan Zhang, Jianguo Wang, Pan Li, "SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning," VLDB 2023 (codes)

• Houye Ji, Cheng Yang, Chuan Shi, Pan Li, Heterogeneous Graph Neural Network with Distance Encoding, ICDM 2021 and the full version in TKDE 2023

• Sushee Suresh, Mayank Shrivastava, Arko Mukherjee, Jennifer Neville, Pan Li, "Expressive and Efficient Representation Learning for Ranking Links in Temporal Graphs" WWW 2023 (codes)

Year 2022

• Yuhong Luo, Pan Li, "Neighborhood-aware Scalable Temporal Network Representation Learning," LoG 2022 (best paper award!) (codes) (talks) 

• Pan Li, Jure Leskovec, "The Expressive Power of Graph Neural Networks, " Chapter 5 in "Graph Neural Networks: Foundations, Frontiers, and Applications" edited by Wu, Lingfei and Cui, Peng and Pei, Jian and Zhao, Liang. (Also see the formal book link here: Graph Neural Networks: Foundations, Frontiers, and Applications | SpringerLink)

• Haoyu Wang, Nan Wu, Hang Yang, Cong Hao, Pan Li, "Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation," NeurIPS 2022 (codes)

• Rongzhe Wei, Haoteng Yin, Junteng Jia, Austin R. Benson, Pan Li, "Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective", NeurIPS 2022 (codes)

• Haoteng Yin, Muhan Zhang,  Yanbang Wang, Jianguo Wang, Pan Li, "Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning," VLDB 2022 (codes)

• Guanhong Tao, Guangyu Shen, Yingqi Liu, Shengwei An, Qiuling Xu, Shiqing Ma, Pan Li, Xiangyu Zhang, "Better Trigger Inversion Optimization in Backdoor Scanning," CVPR 2022 (oral) (codes)

• Nan Wu, Hang Yang, Yuan Xie, Pan Li, Cong Hao, "High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing," DAC 2022

• Yunyu Liu, Jianzhu Ma, Pan Li, "Neural Higher-order Pattern (Motif) Prediction in Temporal Networks," WWW 2022. (codes)

• Muyi Liu, Pan Li, "SATMargin: Practical Maximal Frequent Subgraph Mining via Margin Space Sampling," WWW 2022

• Siqi Miao,  Miaoyuan Liu, Pan Li, "Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism," ICML 2022 (codes) 

• Haorui Wang, Haoteng Yin, Muhan Zhang, Pan Li, "Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks," ICLR 2022. (code)

• Mingyue Tang*, Carl Yang*, Pan Li, "Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction," ICLR 2022 (code).

• Hejie Cui*, Zijie Lu*, Pan Li, and Carl Yang, On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs, KDD DLG workshop 2021, CIKM 2022

Year 2021

• Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, Long Jin, "Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning," NeurIPS 2021.(code) 

• Muhan Zhang, Pan Li,  "Nested Graph Neural Networks," NeurIPS 2021. (code)

• Susheel Suresh, Pan Li, Cong Hao, Jennifer Neville, "Adversarial Graph Augmentation to Improve Graph Contrastive Learning," NeurIPS 2021. (codes)

• Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, Pan Li, "Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks," ICLR 2021.  (codes) 

•  Eli Chien*, Jianhao Peng*, Pan Li, Olgica Milenkovic, "Adaptive Universal Generalized PageRank Graph Neural Network, " ICLR 2021. (codes) 

• Yuhong Li, Callie Hao, Pan Li, Jinjun Xiong, Deming Chen "Generic Neural Architecture Search via Regression, " NeurIPS 2021 (spotlight presentation, acceptance rate < 3%) (code)

• Lixiang Li, Yao Chen, Zacharie Zirnheld, Pan Li, and Cong Hao, "MeLoPPR: Software/Hardware Co-design for Memory-efficient Low-latency Personalized PageRank", DAC 2021. (codes)

• Susheel Suresh, Vinith Budde, Jennifer Neville, Pan Li, Jianzhu Ma, "Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns, " KDD 2021. (codes)

• Andrew Z Wang, Rex Ying, Pan Li, Nikhil Rao, Karthik Subbian, Jure  Leskovec, "Bipartite Dynamic Representations for Abuse Detection, " KDD 2021.

• Meng Liu, Nate Veldt, Haoyu Song, Pan Li, David Gleich, "Strongly Local Hypergraph Diffusions for Clustering and Semi-Supervised Learning", WWW 2021.  (codes) 

• Yanbang Wang*, Pan Li*, Chongyang Bai, Jure Leskovec, "TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction Networks, " WWW 2021. 

• Siddharth Bhatia, Arjit Jain, Pan Li, Ritesh Kumar, Bryan Hooi, "MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams," WWW 2021 (best paper runner-up).

• Yen-Yu Chang*, Pan Li*, Rok Sosic, Mohamed Ibrahim, Marco Schweighauser and Jure Leskovec, "F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams," selected for oral presentation, WSDM 2021. (selected for oral presentation) (codes)

• Eli Chien, Pan Li, Olgica Milenkovic, "Landing Probabilities of Random Walks for Seed-set Expansion in Hypergraphs", ITW 2021.

Year 2020

• Pan Li, Yanbang Wang, Hongwei Wang, Jure Leskovec, "Distance Encoding -- Design Provably More Powerful GNNs for Structural Representation Learning," NeurIPS 2020. (codes)(slides)

• Tailin Wu*, Hongyu Ren*, Pan Li, Jure Leskovec, "Graph Information Bottleneck," NeurIPS 2020. (codes) (slides)

• Pan Li, Niao He and Olgica Milenkovic, "Quadratic Decomposable Submodular Function Minimization: Theory and Practice", JMLR 2020, NeurIPS 2018. (slides, slides (PhD defense) , codes, poster, neurips2018-version) --- Computation and Analysis of PageRanks over Hypergraphs!

Years before 2020

• Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li, "Conditional Structure Generation through Graph Variational Generative Adversarial Nets," NeurIPS 2019. 

• Pan Li*, Eli Chien* and Olgica Milenkovic, "Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection," NeurIPS 2019. (slides, poster, talk)  

• Eli Chien, Huozhi Zhou and Pan Li, "HS2: Active Learning on Hypergraphs," AISTATS 2019. (full-version)

• Pan Li and Olgica Milenkovic, "Revisiting Decomposable Submodular Function Minimization with Incidence Relations," NeurIPS 2018. (slides (PhD defense) , codes, poster, full-version) --- Computation of min-cuts over Hypergraphs!

• Pan Li and Olgica Milenkovic, "Submodular Hypergraph: p-Laplacian, Cheeger Inequalities and Spectral Clustering," ICML 2018. (slides, slides (PhD prelim), codes, poster, full-version) --- Spectral Theory for Hypergraphs!

• Pan Li and Olgica Milenkovic, "Inhomogoenous Hypergraph Clustering with Applications," NeurIPS 2017 (spotlight presentation, acceptance rate < 4.6%). (slides, slides (PhD prelim), codes, poster, full-version) --- Applications motivate the line of research for Hypergraphs and preliminary spectral theory!

• Pan Li, Gregory Puleo and Olgica Milenkovic, "Motif and Hypergraph Correlation Clustering," IEEE Transactions on Information Theory (TIT) 2019. (infocom2017-version, slides (phd qual))

• Carl Yang, Yichen Feng, Pan Li, Yu Shi, and Jiawei Han, "Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights," ICDM, 2018 (regular paper, acceptance rate < 8.9%).

•  Pan Li, Zhen Qin, Xuanhui Wang and Donald Metzler, "Combining Decision Trees and Neural Networks for Learning to Rank in Personal Search," KDD 2019 (applied data science track, selected for oral presentation, acceptance rate < 6.5%).  (my intern project in Google) (short video, slides)

• Pan Li and Olgica Milenkovic, "Efficient Ranking Aggregation with Lehmer Code," AISTATS 2017. (poster, full-version)

• Pan Li and Olgica Milenkovic, "Multiclass Minmax Rank Aggregation," ISIT 2017. (slides)